{"id":"https://openalex.org/W7220764843","doi":null,"title":"Energy Consumption Forecasting in Smart Homes Using Deep Learning Models","display_name":"Energy Consumption Forecasting in Smart Homes Using Deep Learning Models","publication_year":2026,"publication_date":"2026-03-18","ids":{"openalex":"https://openalex.org/W7220764843"},"language":"en","primary_location":{"id":"pmh:oai:amresearchjournal.com:article/1761","doi":null,"is_oa":false,"landing_page_url":"https://www.amresearchjournal.com/index.php/Journal/article/view/1761","pdf_url":null,"source":{"id":"https://openalex.org/S5407043348","display_name":"Annual Methodological Archive Research Review","issn_l":"3007-3189","issn":["3007-3189","3007-3197"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"10.66021/","raw_type":"info:eu-repo/semantics/article"},"volume":null,"type":"article","indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5157741864","display_name":"Hafiz Muhammad Naeem Ahmed Aqeel","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hafiz Muhammad Naeem Ahmed Aqeel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5157735344","display_name":"Dr. Ahmad Khan","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmad Khan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5153700929","display_name":"Muhammad Mursaleen Akbar","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Muhammad Mursaleen Akbar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5157743283","display_name":"Muhammad Ayaz","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Muhammad Ayaz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76120536,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.5414999723434448,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.5414999723434448,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":0.44119998812675476,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10121","display_name":"Building Energy and Comfort Optimization","score":0.009800000116229057,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/smart-home","display_name":"home automation","score":0.9860000014305115},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.5899999737739563},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9779999852180481},{"id":"https://openalex.org/keywords/hybrid-cnn-lstm-model","display_name":"CNN-LSTM hybrid model","score":0.8980000019073486},{"id":"https://openalex.org/keywords/peak-shaving","display_name":"peak shaving","score":0.621999979019165},{"id":"https://openalex.org/keywords/occupancy-detection","display_name":"occupancy detection","score":0.6169999837875366},{"id":"https://openalex.org/keywords/weather-effects-on-energy-demand","display_name":"weather effects on energy demand","score":0.7490000128746033},{"id":"https://openalex.org/keywords/occupant-behavior","display_name":"occupant behavior","score":0.6510000228881836},{"id":"https://openalex.org/keywords/time-series-forecasting","display_name":"time series forecasting","score":0.5709999799728394}],"concepts":[{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5874999761581421},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5835999846458435},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5806000232696533},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.46790000796318054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42649999260902405},{"id":"https://openalex.org/C30772137","wikidata":"https://www.wikidata.org/wiki/Q5164762","display_name":"Consumption (sociology)","level":2,"score":0.4226999878883362},{"id":"https://openalex.org/C7817414","wikidata":"https://www.wikidata.org/wiki/Q1779504","display_name":"Energy management","level":3,"score":0.41749998927116394},{"id":"https://openalex.org/C10558101","wikidata":"https://www.wikidata.org/wiki/Q689855","display_name":"Smart grid","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.39489999413490295},{"id":"https://openalex.org/C83931994","wikidata":"https://www.wikidata.org/wiki/Q1149653","display_name":"Building automation","level":2,"score":0.3926999866962433},{"id":"https://openalex.org/C2779438525","wikidata":"https://www.wikidata.org/wiki/Q5255048","display_name":"Demand response","level":3,"score":0.38199999928474426},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3425000011920929},{"id":"https://openalex.org/C2781260460","wikidata":"https://www.wikidata.org/wiki/Q6139999","display_name":"Energy management system","level":4,"score":0.32499998807907104},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.31929999589920044},{"id":"https://openalex.org/C66204764","wikidata":"https://www.wikidata.org/wiki/Q219416","display_name":"Sustainability","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C507571656","wikidata":"https://www.wikidata.org/wiki/Q848436","display_name":"Home automation","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2888000011444092},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.28110000491142273},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.27880001068115234},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.27649998664855957},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.27300000190734863},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.2671000063419342},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.2590000033378601},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.25429999828338623},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25429999828338623},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:amresearchjournal.com:article/1761","doi":null,"is_oa":false,"landing_page_url":"https://www.amresearchjournal.com/index.php/Journal/article/view/1761","pdf_url":null,"source":{"id":"https://openalex.org/S5407043348","display_name":"Annual Methodological Archive Research Review","issn_l":"3007-3189","issn":["3007-3189","3007-3197"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"10.66021/","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.9853000044822693},{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.685699999332428}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,237],"rapid":[1],"growth":[2],"of":[3,19,36,62,74,100,127,173,244,282,297],"intelligent":[4,329],"domestic":[5,82,311],"environments":[6],"and":[7,10,33,47,95,104,130,150,181,205,230,249,272,290,320,335],"overconnected":[8],"appliances":[9],"sophisticated":[11,149],"automation":[12],"systems":[13],"has":[14],"forever":[15],"changed":[16],"the":[17,34,60,72,81,87,93,98,124,148,159,162,188,192,242,280,306,328],"paradigm":[18],"residential":[20,254,338],"energy":[21,64,223,330,339],"stewardship.":[22],"Devices":[23],"like":[24],"adaptive,":[25],"thermostats,":[26],"dynamic":[27],"lighting":[28],"arrays,":[29],"advanced":[30],"metering":[31],"infrastructure":[32],"Internet":[35],"Things":[37],"-":[38,158],"enabled":[39],"household":[40],"units":[41],"providing":[42,300],"operators":[43],"with":[44],"real-time":[45,310],"telemetry":[46],"control":[48],"directives":[49],"curtail":[50],"operating":[51],"expenditure":[52],"while":[53,258],"attenuate":[54],"ecological":[55],"footprints.":[56],"Within":[57],"this":[58,315],"context,":[59],"development":[61],"correct":[63],"forecasts":[65],"becomes":[66],"a":[67,107,171,301,309,318],"key":[68],"pillar":[69],"on":[70,161],"which":[71,218],"optimization":[73],"consumption":[75,163,168,177],"can":[76],"be":[77,90],"built":[78],"to":[79,85,89,96,116,154],"allow":[80],"management":[83],"subsystems":[84],"predict":[86],"demand":[88,256],"met":[91],"in":[92,106,112,221,233,308],"future":[94],"orchestrate":[97],"strategies":[99],"heating,":[101],"ventilation,":[102],"cooling":[103],"illumination":[105],"time-reactive":[108],"way.":[109],"Such":[110],"predisposition":[111],"governance":[113],"is":[114,169],"conducive":[115],"massive":[117],"peak":[118],"load":[119],"refreshment,":[120],"by":[121],"successfully":[122],"augmenting":[123],"efficient":[125],"taping":[126],"renewable":[128],"fluxes":[129],"enforcing":[131],"central":[132],"sustainability":[133],"priorities.":[134],"Conventional":[135],"s":[136],"classical":[137],"time":[138],"spacecraft":[139],"techniques,":[140],"however,":[141],"have":[142],"often":[143],"performed":[144],"poorly":[145],"at":[146],"grasping":[147],"nonlinear":[151],"interdependencies":[152],"common":[153],"smart":[155,312],"dwelling":[156],"ecosystems":[157],"dimensions":[160],"profile":[164],"being":[165],"Trujillo":[166],"as":[167,267],"very":[170],"function":[172],"occupant":[174],"behavior,":[175],"appliance":[176,273],"styles,":[178],"meteorological":[179],"variables,":[180],"diel":[182],"rhythms.":[183],"To":[184],"overcome":[185],"these":[186],"challenges,":[187],"current":[189],"study":[190,316],"employs":[191],"state-of-the-art":[193],"deep":[194,322],"learning":[195],"architectures,":[196],"that":[197,279,326],"is,":[198],"Long":[199],"Short-Term":[200],"Memory":[201],"(LSTM)":[202],"recurrent":[203],"networks":[204],"Convolutional":[206],"Neural":[207],"Network":[208],"(CNN)":[209],"architectures.":[210],"LSTM":[211],"architectures":[212],"well":[213],"capture":[214],"long-lasting":[215],"temporal":[216],"continuities,":[217],"are":[219,241],"inherent":[220],"sequential":[222],"datasets,":[224],"whereas":[225],"CNNs":[226],"models":[227],"summarize":[228],"local":[229],"structural":[231],"motifs":[232],"multivariate":[234],"chronological":[235],"streams.":[236],"proposed":[238],"research":[239],"questions":[240],"interrogation":[243],"pure":[245],"LSTM,":[246],"standalone":[247],"CNN,":[248],"hybrid":[250],"CNN-LSTM":[251,285],"configurations":[252],"for":[253,304],"power":[255],"forecasting":[257,324],"taking":[259],"into":[260],"consideration":[261],"some":[262],"ancillary":[263],"context":[264],"indicators":[265],"such":[266],"climatic":[268],"states,":[269],"occupancy":[270],"flags,":[271],"utilization":[274],"metrics.":[275],"Empirical":[276],"analytics":[277],"show":[278],"combination":[281],"COM-Hyb":[283],"blueprint":[284],"assembles":[286],"higher":[287],"predictive":[288],"fidelity":[289],"immune":[291],"states":[292],"over":[293],"heterogeneous":[294],"analysis":[295],"regimes":[296],"cadences,":[298],"thus":[299],"potential":[302],"candidate":[303],"employing":[305],"model":[307],"platform.":[313],"Collectively,":[314],"provides":[317],"pragmatic":[319],"efficacious":[321],"learning-based":[323],"methodology":[325],"enhances":[327],"governance,":[331],"reduces":[332],"fiscal":[333],"outlays":[334],"promotes":[336],"sustainable":[337],"utilization.":[340]},"counts_by_year":[],"updated_date":"2026-10-08T09:06:37.294493","created_date":"2026-10-07T00:00:00"}